Global adaptive neural backstepping control of a flexible hypersonic vehicle with disturbance estimation

Abstract

PurposeThis paper aims to discuss the precise altitude and velocity tracking control of a hypersonic vehicle, a global adaptive neural backstepping controller was studied based on a disturbance observer (DOB).Design/methodology/approachThe DOB combined with a radial basis function (RBF) neural network (NN) was used to estimate the disturbance terms that are generated by the flexible modes of the hypersonic vehicle system. A global adaptive neural method was introduced to approximate the unknown system dynamics, with robust control terms pulling the system transient states back into the neural approximation domain externally.FindingsThe globally uniformly ultimately bounded for all signals of a closed-loop system can be guaranteed by the proposed control algorithm. Additionally, the command filtered backstepping methods can avoid the explosion of the complexity problem caused by the backstepping design process. In addition, the effectiveness of the proposed controller can be verified by the simulation used in this study.Research limitations/implicationsNormally lateral dynamics issue should be discussed in the process of control system designed, the lateral dynamics are not included in the nonlinear dynamic model of hypersonic vehicle used in this paper, merely the longitudinal flight dynamics are discussed in this paper.Originality/valueThe flexible states in rigid modes are considered as the disturbance of the system, which is estimated by structuring DOB with NN approximations. The compensating tracking error and prediction error are used in the update law of RBF NN weight. The differential explosions complexity derived from the backstepping procedure is dealt with by using command filters.